Lung Cancer remains the leading cause of mortality cancer. In 1999, there were approximately 170,000 new cases of lung cancer in the U.S., where approximately one in every eighteen women and approximately one in every twelve men develop lung cancer. Early detection of lung tumors (visible on chest film as nodules) may increase the patient's chance of survival, but detecting nodules is a complicated task. Nodules show up as relatively low-contrast white circular objects within the lung fields. The difficulty for computer aided image data search schemes is distinguishing true nodules from (overlapping) shadows, vessels and ribs.
The early stage detection of lung cancer remains an important goal in medical research. Regular chest radiography and sputum examination programs haven proven ineffective in reducing mortality rates. Although screening for lung cancer with chest X-rays can detect early lung cancer, such screening can also possibly produce many false-positive test results, causing needless extra tests.
At present, low-dose spiral computed tomography (LDCT) is of prime interest for screening (high risk) groups for early detection of lung cancer and is being studied by various groups, including the National Cancer Institute. LDCT provides chest scans with very high spatial, temporal, and contrast resolution of anatomic structures and is able to gather a complete 3D volume of a human thorax in a single breath-hold. Hence, for these reasons, in recent years most lung cancer screening programs are being investigated in the United States and Japan with LDCT as the screening modality of choice.
Automatic screening of image data from LDCT typically involves selecting initial candidate lung abnormalities (nodules). Next, the false candidates, called false positive nodules (FPNs), are partially eliminated while preserving the true ones (TPNs).
When selecting initial candidates, conformal nodule filtering or unsharp masking can enhance nodules and suppress other structures to separate the candidates from the background by simple thresholding or multiple gray-level thresholding techniques. A series of 3D cylindrical and spherical filters are used to detect small lung nodules from high resolution CT images. Circular and semicircular nodule candidates can be detected by template matching. However, these spherical, cylindrical, or circular assumptions are not adequate for describing general geometry of the lesions. This is because their shape can be irregular due to the speculation or the attachments to the pleural surface (i.e., juxtapleural and peripheral) and vessels (i.e., vascularized). Morphological operators may be used to detect lung nodules. The drawbacks to these approaches are the difficulties in detecting lung wall nodules. Also, there are other pattern recognition techniques used in detection of lung nodules such as clustering, linear discriminant functions, rule-based classification, Hough transforms, connected component analysis of thresholded CT slices, graylevel distance transforms, and patient-specific a priori models.
The FPNs may be excluded by feature extraction and classification. Such features as circularity, size, contrast, or local curvature that are extracted by morphological techniques, or artificial neural networks (ANN), can be used as post-classifiers. Also, there is a number of classification techniques used in the final stage of the nodule detection systems to reduce the FPNs such as: rule-based or linear classifiers; template matching; nearest cluster; and Markov random field.
Monitoring is a largely unresolved problem in lung scans, and there exist similar issues in monitoring other nodules or shaped targets. Nodules are at issue, for example, in other types of medical imaging analysis. Also shaped target monitoring has broader applications.